{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "994f27c1",
   "metadata": {},
   "source": [
    "# 数据分布\n",
    "## 什么是数据分布？\n",
    "数据分布是一个所有可能的值的列表，以及每个值的出现频率。\n",
    "\n",
    "这种列表在统计学和数据科学工作中非常重要。\n",
    "\n",
    "随机模块提供的方法可以返回随机生成的数据分布。\n",
    "\n",
    "## 随机分布\n",
    "随机分布是一组遵循某种概率密度函数的随机数。\n",
    "\n",
    "概率密度函数。一个描述连续概率的函数。即一个数组中所有值的概率。\n",
    "\n",
    "我们可以使用随机模块的choice()方法根据定义的概率生成随机数。\n",
    "\n",
    "choice()方法允许我们指定每个值的概率。\n",
    "\n",
    "概率由0和1之间的数字设定，其中0表示该值永远不会出现，1表示该值总是出现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86067bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import random\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9a428c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 7 7 7 7 5 7 7 7 7 7 5 7 7 5 5 7 5 3 7 7 7 7 5 7 7 3 3 7 7 7 5 7 7 5 3 5\n",
      " 3 7 7 5 5 7 7 5 5 7 7 7 7 7 7 5 3 3 7 7 3 7 7 5 7 5 7 3 5 7 5 7 7 7 3 7 5\n",
      " 7 7 7 7 5 5 3 5 5 7 5 7 3 5 7 5 5 7 5 5 7 7 7 3 7 7]\n"
     ]
    }
   ],
   "source": [
    "x = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(100))\n",
    "\n",
    "print(x)\n",
    "# 所有概率的总和应该是1。\n",
    "# 即使运行上面的例子100次，数值9也不会出现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7beaec0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7 5 7 7 7]\n",
      " [3 7 3 7 5]\n",
      " [5 7 7 7 7]]\n"
     ]
    }
   ],
   "source": [
    "# 可以通过在size参数中指定形状来返回任何形状和大小的数组。\n",
    "x = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(3, 5))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab27bcf",
   "metadata": {},
   "source": [
    "# 随机排列组合\n",
    "## 元素的随机排列\n",
    "例如，[3, 2, 1]是[1, 2, 3]的一个排列，反之亦然。\n",
    "\n",
    "NumPy随机模块为此提供了两个方法：shuffle()和permutation()。\n",
    "\n",
    "## 洗牌数组\n",
    "洗牌是指在原地改变元素的排列，即在数组本身。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3387edfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 2 5 1 3]\n"
     ]
    }
   ],
   "source": [
    "# shuffle()方法会对原始数组进行修改。\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "\n",
    "random.shuffle(arr)\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca9af2b",
   "metadata": {},
   "source": [
    "## 生成数组的排列组合\n",
    "生成以下数组元素的随机排列组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c8652e73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 1 4 5 2]\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# permutation()方法返回一个重新排列的数组（并使原始数组不发生变化）。\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "\n",
    "newarr = random.permutation(arr)\n",
    "\n",
    "print(newarr)\n",
    "print(newarr.base)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca777a4",
   "metadata": {},
   "source": [
    "# 正态分布\n",
    "正态分布是最重要的分布之一。\n",
    "\n",
    "它也被称为高斯分布，以德国数学家卡尔-弗里德里希-高斯的名字命名。\n",
    "\n",
    "它适合许多事件的概率分布，例如：IQ分数、心跳等。\n",
    "\n",
    "使用random.normal()方法可以得到一个正态数据分布。\n",
    "\n",
    "它有三个参数。\n",
    "\n",
    "loc - (Mean)钟的峰值存在的地方。\n",
    "\n",
    "scale - （标准偏差）图形分布应该有多平坦。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "108935ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.63378485 -1.588337   -1.22687188]\n",
      " [ 0.06666984  0.60201045 -0.54424427]]\n"
     ]
    }
   ],
   "source": [
    "x = random.normal(size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0dcfff54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.92805491 -2.38029617  2.1944194 ]\n",
      " [ 2.69954435 -0.40749948 -0.00803978]]\n"
     ]
    }
   ],
   "source": [
    "# 生成一个大小为2x3的随机正态分布，平均数为1，标准差为2。\n",
    "x = random.normal(loc=1, scale=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "943014b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.normal(size=1000), hist=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d0244f",
   "metadata": {},
   "source": [
    "# 二项式分布\n",
    "二项分布是一种离散分布。\n",
    "\n",
    "它描述了二元情景的结果，例如抛硬币，它要么是正面，要么是反面。\n",
    "\n",
    "它有三个参数。\n",
    "\n",
    "n - 试验的数量。\n",
    "\n",
    "p - 每个试验出现的概率（例如抛硬币，每次0.5）。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4831481e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 3]\n"
     ]
    }
   ],
   "source": [
    "# 给出10次抛硬币的试验，产生2个数据点\n",
    "x = random.binomial(n=10, p=0.5, size=2)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "36efd5af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.binomial(n=10, p=0.5, size=1000), hist=True, kde=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efaae9e2",
   "metadata": {},
   "source": [
    "正态分布和二项分布的区别\n",
    "\n",
    "主要区别在于，正态分布是连续的，而二项分布是离散的，但如果有足够的数据点，它将与正态分布相当相似。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cae8692e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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wtzsJ0eFMGRhEU2ucS5+rQdyQPQ+AK9M6UlJZa2tEmAtiBcIEl6qTsGMeJ3tdxyfb87lrVHeiI9xOp2o5Me2hx5ivCsTYPh0Ic4ld7mouiBUIE1x2fAy1FcyqHE24y8W9o1OcTtTy0qbA8W1QuIc2UeFkpLTj82wbqDbnzwqECS5Z7+Npk8zvdrRlxpCudAy2ifmaIm2q97NvKdLxfZLYfqSE/FK73NWcHysQJnhUnICcRWxqcyUVNR6+Ob6n04mc0T4VOqb/s5updwcAVuXa7K7m/FiBMMFjx8fgqeH3RwYyoW8SaZ3jnU7knLQpsG8llBcyqFsC8VFhrMzNdzqVaWWsQJjgsfU9SmOSWVqWzIPjU51O46y0aaB1sGsBbpcwqmciX+TYGYQ5P1YgTHAoK0B3L+GjulH069yGcb5ulZDVdSjEdf5nN1OvRPYXlnOgsNzhYKY1sQJhgsP2uYjW8XrJML45viciQT7v0rm4XJA22bsUaW3VV+MQX+RYN5NpOisQJjhkvc+RsG7kx/Vl+uAgXDXuQqRNhepS2Luc3h3j6BgfyRc2UG3OgxUI0/qVHkf3LuedyhHcOzaViDD7bw1A6uUQHgs75iEijO3dgVW5+XhX9TXm3OwnybR+2z5A1MNC11juHNnD6TSBIzwKel/lvR9ClTG9EskvrSb72Emnk5lWwgqEafWqN71LjnZj2PAxJMSEOx0nsKRNhZOH4UhmvXEI62YyTePXAiEik0UkW0RyROSxRvZHisgs3/7VIpLi254iIhUikun7+LM/c5pWrOQI4Ye+5KO6UXxjXIjeGHc2fa4FccGOeXRtG01qh1hW2kC1aSK/FQgRcQMvAFOAdOB2EUlv0OwB4ISq9gaeBZ6uty9XVYf4Ph72V07TulVtfg9BKep5Pd0TY5yOE3hiE+GSUV9NuzGmVyKr9xTa9N+mSfx5BjESyFHV3apaDcwEZjRoMwN41fd4DjBRQv76RHM+itbNZrvnEqZPusLpKIGr31Q4tgVO7GNMrw6UVtWy5VCx06lMK+DPAtENOFDv+UHftkbbqGotUAwk+valishGEVkqIuMbewMReUhE1onIurw8m60y1NQVHyGpaBOb4i9nWPd2TscJXKcm79v5KaN6tgdg1W4bhzDnFqiD1EeA7qo6FHgUeEtE2jRspKovqWqGqmYkJSW1eEjjrO2fv4ULJXnMrU5HCWyJvaBDGmTPIzEukn6d423iPtMk/iwQh4D6az0m+7Y12kZEwoAEoEBVq1S1AEBV1wO5QF8/ZjWtkCfrA/ZJN0aPGud0lMCXNgX2roDKYkb1TGTt3kKqa20cwpydPwvEWqCPiKSKSARwGzC3QZu5wL2+xzcDi1VVRSTJN8iNiPQE+gC7/ZjVtDKbduaSXr2F4pQpuN2BeiIcQNKmgqcWchYyplcilTUeMm0ZUnMOfvvJ8o0pPALMB7YDs1U1S0SeFJHpvmYvA4kikoO3K+nUpbATgM0ikol38PphVS30V1bT+mxb/DZh4qHPFXc4HaV1SM6AmA6Q/QmXpSYiYutDmHML8+cXV9V5wLwG2x6v97gSuKWR170LvOvPbKb1yjtZRdfDCzgR1YV23Yc5Had1cLmh72TY8SEJN8DArgmszM3n+5P6OJ3MBDA7NzetzvurshgtW5D06WBXRTdd2hSoLIZ9KxndK5GN+4uorKlzOpUJYFYgTKtSW+fh8Op/ECF1tB12k9NxWpdeV0JYFGR/wuheiVTXeVi/74TTqUwAswJhWpUF244xqnolldEdIXmE03Fal4hY6HkFZM9jRI92uF1iy5Cas7ICYVqVmV/s4Ar3JiIGzvAuimPOT9oUKNpHXPEuBicn2EC1OSv7CTOtRm5eKTH7PyeKalzp08/9AnO6vpO9n7PnMaZXBzYdLKa0qtbZTCZgWYEwrca76w8y1b0GT3QidB/jdJzWKb4zdBsO2fMY3SuROo+ydq9dQW4aZwXCtAp1HuXjDXuYFJaJq/80cPv1Cu3gljYVDq1nePsqItwu62YyZ9SkAiEi74nINBGxgmIcsSq3gNTSDURrBfS73uk4rZtv8r6oPQsY2r2tDVSbM2rqL/w/AncAu0TkKRFJ82MmY07z7oaDTI3YiIbHQuoEp+O0bh37Q9sekP0JY3p1IOtwCcXlNU6nMgGoSQVCVReq6p3AMGAvsFBEVorI/SJiazwavzpZWcMnWw9zTfgmpNeV3rWWzYUT8Z5F7F7C2O7RqMKXe6ybyZyuyV1GIpII3Ad8E9gIPIe3YCzwSzJjfD7ZcpRetXtoW3Pce5mmuXhpU6C2ksE1G4kKt3EI07imjkG8DywHYoDrVXW6qs5S1e8Ccf4MaMycDQe5OX4LinjXWDYXr8cYiEogfNenjEhpbwXCNKqpZxB/UdV0Vf21qh4BEJFIAFXN8Fs6E/KOFFewZk8hUyMykeQREGcLQzULdzj0uQZ2fsqYnm3JPnaS/NIqp1OZANPUAvHLRratas4gxjTm061H6UQhnUq3Q9pkp+MEl7QpUJ7PxDjvysBf2jKkpoGzFggR6Swiw4FoERkqIsN8H1fg7W4yxq8+2XKUO9pu8z45tbayaR69J4ErjN4nlhEXGcZK62YyDZzrbqNr8Q5MJwPP1Nt+EviJnzIZA8DxkkrW7ivkqc6bIaoHJPVzOlJwiUqAlHG4dn7KqJ7TWbHL7ocw/+qsZxCq+qqqXgncp6pX1vuYrqrvtVBGE6LmZx0lSitJKVnrPXuwtR+aX9pUyM9mWrcy9heWs6+gzOlEJoCcq4vpLt/DFBF5tOFHC+QzIeyTrUe5uW0OrroqG3/wF99lwxN0HQDL7CzC1HOuQepY3+c4IL6Rj7MSkckiki0iOSLyWCP7I0Vklm//ahFJabC/u4iUisgPm/LNmOBRUFrFl7sL+HqbLRDZxibn85e23aHTINofXEhyu2iW78xzOpEJIGcdg1DVF32f//t8v7CIuIEXgKuBg8BaEZmrqtvqNXsAOKGqvUXkNuBp4NZ6+58BPjnf9zat32fbjqHqod/JL72DqWERTkcKXmlTkOW/5dr+EczOKqCmzkO426ZdM02/Ue5/RaSNiISLyCIRyavX/XQmI4EcVd2tqtXATGBGgzYzgFd9j+cAE0W8Hc0icgOwB8hq4vdigsi8LUe4tu1hwivy7O5pf0ubAuphesxWTlbVsulAkdOJTIBo6p8J16hqCXAd3rmYegM/OsdrugEH6j0/6NvWaBtVrQWKgUQRiQP+EzjvMxfT+pVU1rAqt4C722WBuL1nEMZ/ugyB+C6kn/wCl9g4hPmnphaIU11R04B3VLXYT3lOeQJ4VlVLz9ZIRB4SkXUisi4vz/pOg8XynfnUepShlV9C99EQ097pSMHN5YK0KYTvWczw5FiW77KfJePV1ALxkYjsAIYDi0QkCag8x2sOAZfUe57s29ZoGxEJAxKAAuAy4H9FZC/wA+AnIvJIwzdQ1ZdUNUNVM5KSbAqGYLFoxzHSo08QcyLbupdaStpUqC7l9qS9bDpQRHGFTf9tmj7d92PAGCBDVWuAMk4fT2hoLdBHRFJFJAK4DZjboM1c4F7f45uBxeo1XlVTVDUF+D3wK1V9vilZTetW51GWZOfxQFK2d4MViJaRMh7CYxlXtxaPwipbRMhwfkuO9gNuFZF78P4yv+ZsjX1jCo8A84HtwGxVzRKRJ0Xk1IrzL+Mdc8gBHgVOuxTWhJbMA0UUllUznnWQ2AcSezkdKTSER0Hvq0g6vJj4SDdL7XJXw7mn2gBARF4HegGZQJ1vswKvne11qjoPmNdg2+P1HlcCt5zjazzRlIwmOCzecYwEVwVJ+Wth1LedjhNa0qYi2z/kju4n+GBHOKqK2N3rIa2pK79nAOmqqv4MY8yi7ce5r2MuUlRj3Ustrc+1IC6mR2XyYskEth0pYUDXBKdTGQc1tYtpK9DZn0GMOVRUwY6jJ7kuMhOi20PySKcjhZbYROg+hrQTSwD4fMdxZ/MYxzW1QHQAtonIfBGZe+rDn8FM6Fm84zhu6uhZtNK7mI27qSe4ptn0v46w/B1c26WMz7NtHCLUNfUn8Al/hjAGYPH2Y0xtux93ZZF1Lzml3zT49DHubLOF+3bGcqKsmnaxNs1JqGrqZa5L8d5BHe57vBbY4MdcJsRU1tSxancBt7XJAlc49LrK6UihqW136DKY4RUr8Ch2NVOIa+pcTA/inSvpRd+mbsA//JTJhKC1ewuprPEwpOJLSBkHUW2cjhS6+l1P7PENpMWWsdjGIUJaU8cg/g0YC5QAqOouoKO/QpnQs2xnHn3dR4k9uduWFnVa/+sAeDBpB0t35lFb53E4kHFKUwtElW9GVuCraTHsklfTbJbvyueexO3eJ7Y4kLOS+kH7XkzwfElxRQ2ZNrtryGpqgVgqIj8BokXkauAd4EP/xTKh5FhJJTuOnuQqWQ+dBnr7wY1zRKD/dSTlr6adq5yF262bKVQ1tUA8BuQBW4Bv4b07+r/8FcqElmU780iglC4lm6CvnT0EhH7XI55avtl5Fwu2HXU6jXFIky5zVVWPiPwD+Ieq2mUNplkt25XP9TFZiKfOLm8NFN2GQ1xnpoWt5zeHB5ObV0qvpDinU5kWdtYzCPF6QkTygWwg27ea3ONne50xTeXxKCt25XFT7GaI7QhdhzkdyYB3jYh+0+he+AWRVLNg2zGnExkHnKuL6d/xXr00QlXbq2p7vGs1jBWRf/d7OhP0th4uprS8goEVa6Hvtd5fTCYw9L8OV20Fd3XIsQIRos7103g3cLuq7jm1QVV3A3cB9/gzmAkNy3bmMdK1nfDaUru8NdCkjIeoBG6KyWTD/hPknaxyOpFpYecqEOGqetrKIb5xiHD/RDKhZNnOfG5tsxXCoqDnFU7HMfW5w6HvZPoWr8CldSzabmcRoeZcBaL6AvcZc04nK2vYsL+QCbreWxwiYpyOZBrqdx1hVUVMa7PbuplC0LmuYhosIiWNbBcgyg95TAhZlVtATz1A26rD0Pf/OR3HNKb3RAiL4q6ELdyVk0ZZVS2xkTbLbqg46xmEqrpVtU0jH/Gqal1M5qIs25XHlPCN3id2/0NgioiFXhMZXLaC6to6ltnkfSHFLhkxjlm2M5/p0Zug61Bo08XpOOZM+l9HZPlRxsUcYN5Wu2kulPi1QIjIZBHJFpEcEXmskf2RIjLLt3+1iKT4to8UkUzfxyYRudGfOU3L21dQRkXhIXpW7bCrlwJd2hRwhfFg4mYWbz9GZU3duV9jgoLfCoSIuIEXgClAOnC7iKQ3aPYAcEJVewPPAk/7tm8FMlR1CDAZeNE3QaAJEst25jHJvQFBvYvUmMAV3Q56XsFl5csoq661NSJCiD/PIEYCOaq62zcT7ExgRoM2M4BXfY/nABNFRFS1XFVrfdujsJljg87SnflMj9yItkuBjg3/bjABZ8CNRJUdZEz0AeZtOeJ0GtNC/FkgugEH6j0/6NvWaBtfQSgGEgFE5DIRycI7QeDD9QrGV0TkIRFZJyLr8vLsr5rWorrWw6bcA4zQLUi/67yzh5rAljYVXGE8lLiZRduPWzdTiAjYQWpVXa2qA4ARwI9F5LTLalX1JVXNUNWMpKSklg9pLsjG/ScYUbuBMK2x7qXWIqa9t5upYjmlVTUs33Xa/bMmCPmzQBwCLqn3PNm3rdE2vjGGBKCgfgNV3Q6UAgP9ltS0qGW78rjWvR5PdCJccpnTcUxTpd9AdNkBRkdZN1Oo8GeBWAv0EZFUEYkAbgPmNmgzF7jX9/hmYLGqqu81YQAi0gPoB+z1Y1bTglZmH2FSWCauflPA5XY6jmmqftN83UybWLjtGFW11s0U7PxWIHxjBo8A84HtwGxVzRKRJ0Vkuq/Zy0CiiOQAj+JdmAhgHLBJRDKB94HvNDYnlGl9CkqriDu6mlgtg37XOR3HnA9fN9OoyuWcrKphhXUzBT2/XjqqqvPwrj5Xf9vj9R5XArc08rrXgdf9mc04Y0VOPle71lEXFo3bJudrfdJvIDrnEUZH7ePjLclM7N/J6UTGjwJ2kNoEp2XZx7g2bD2u3pMgPNrpOOZ89b8O3BE8nJjJAutmCnpWIEyLUVXyd66hE4VIf+teapWi20GfaxhVtoSyymq+yLFupmBmBcK0mO1HTjKiaiUecUOfa5yOYy7UoFuIrDzOxKgdfLzZ5mYKZlYgTItZujOPa1zrqEke7R3wNK1T32shIp4H227gs6yjdtNcELMCYVrMzm2Z9HUdInLg9HM3NoErPBrSpzOsbBnVVeUs3nHc6UTGT6xAmBZRVlVLtyPzvU9s9tbWb9AthNWUckPsVv6xseH9ryZYWIEwLeLL3QVMkS8p6TAU2l5y7heYwJY6AeI6cX/8GpZk51FcXuN0IuMHViBMi9i6eQMDXPuIGXqT01FMc3C5YeBN9C35kqi6EuZttak3gpEVCNMiYnM/AiBsoK39FDQuvRWXp5pvJKy3bqYgZQXC+N2BwnLGVC7nWMJgSEh2Oo5pLl0GQ6dB3B6+jNV7CjlcVOF0ItPMrEAYv9uwcS3prn24B9nZQ1ARgaF30al0O/1lH3M3HXY6kWlmViCM3+nWfwCQOOK0abdMa3fp18EdwXfafsm76w+iaos/BhMrEMavauo89C9cxN6YQYh1LwWfmPaQNpVrapey7/gJNh0sdjqRaUZWIIxfbd20jjTZR0Wf652OYvxl6N1E1hQxJXwjs9cdOHd702pYgTB+Vb72DepU6DHhTqejGH/pdSXEd+XhhFV8mHmYimqbeiNYWIEw/uPx0PvoPLKihxOTaN1LQcvlhiF30K9sLfFVR/k0y+6JCBZWIIzfHN26mE6ax4nedvVS0Bt2D6jy7fjlzFpr3UzBwgqE8ZuSL9+gTCNJHXer01GMv7XrgfSdzE0sYsPuY+wrKHM6kWkGViCMf9RUkHxkPivCx9K9c5LTaUxLGPlNYmoKmepewzvrDjqdxjQDvxYIEZksItkikiMijzWyP1JEZvn2rxaRFN/2q0VkvYhs8X2+yp85TfOr3PoRMVpOfq8bnI5iWkrPq6B9Tx6JW8LMtfttOdIg4LcCISJu4AVgCpAO3C4i6Q2aPQCcUNXewLPA077t+cD1qjoIuBd43V85jX+UrHmDI9qe1IzJTkcxLcXlgowH6F2VRceynXy61Vaba+38eQYxEshR1d2qWg3MBGY0aDMDeNX3eA4wUUREVTeq6qn79rOAaBGJ9GNW05xKj5N4ZDmfMI4RPa17KaQMvRMNi+Y7sUt4ZeVep9OYi+TPAtENqH85w0HftkbbqGotUAwkNmhzE7BBVasavoGIPCQi60RkXV5eXrMFNxfHs+F13NRxMOUmwt02zBVSotshg25msmcZe/YfYPPBIqcTmYsQ0D+9IjIAb7fTtxrbr6ovqWqGqmYkJdlfqgHBU0fNmr+xsi6dgYMznE5jnDDqO4R5Krk/YjGvrdrndBpzEfxZIA4B9ZcOS/Zta7SNiIQBCUCB73ky8D5wj6rm+jGnaU65i4ksPchMncTE/p2cTmOc0Ckdek/iGxEL+HTTXgpKTzv5N62EPwvEWqCPiKSKSARwGzC3QZu5eAehAW4GFquqikhb4GPgMVX9wo8ZTTPTtX+lQNpyMnUyCdHhTscxThnzXeJrC5mqy5lpN861Wn4rEL4xhUeA+cB2YLaqZonIkyIy3dfsZSBRRHKAR4FTl8I+AvQGHheRTN9HR39lNc2k6ADs+oy3ay7n6oG27nRIS70cOg/i+9HzeWVFLpU1dslraxTmzy+uqvOAeQ22PV7vcSVw2iIBqvpL4Jf+zGb8YMNroMrMuqt4P926l0KaCIz5Ht3ee5BB1WuYvS6Ne0anOJ3KnKeAHqQ2rUhtNWx4jbVhw+makkZSvF2VHPIG3Ii2SeY/Yj/lxaW7qanzOJ3InCcrEKZ5bJ0DpUf5Y/lVTB7Q2ek0JhC4w5HR32FAzVa6FG9kbqYtSdraWIEwF8/jgRW/Jz+2D0s8g7l2oBUI4zP8fjQ2iR/HzuVPS3PxeGxJ0tbECoS5eNnzID+bV1w3cmlyW7q1jXY6kQkUETHImO8yvDaTNnkb+GybTb/RmliBMBdHFVY8Q02b7vwpbxBTBnZxOpEJNBkPoDGJ/GfMXJ5ZsJM6O4toNaxAmIuzdwUcWs/ypNvxiJsZQ7o6ncgEmsg4ZPQjXFa3gejjmXyQ2fB+WROorECYi7PiWTQ2iV8fGcao1ES6WveSaczIB9HodvxXnPcsorrWrmhqDaxAmAu3/0vIXcShft9gV2EdNw5rOBejMT6R8ciY7zKiZh2dizby9pr9TicyTWAFwlwYVVj4BMR14uXqSUSGuZhiVy+Zs7nsYTSuM/8TN4f/W7STsqpapxOZc7ACYS7Mrs9g/ypqx/+I97OKuHZAZ+KjbO4lcxYRscgVj5FWs42hFat4cdlupxOZc7ACYc5fbTXM/wm078XimMkUlddY95JpmqF3Q2Jvnox7l78u3cnBE+VOJzJnYQXCnL81L0JBDkx+ivcyj9MhLpLxvTs4ncq0Bu4wmPhzulTv40bXUn41b7vTicxZWIEw56f4ECx5Gvpcw/EuE1i4/Rg3DOlKmK0cZ5qq//WQPIIfR73Hsi27WZmb73Qicwb2U22aThU+/g/w1MKU/2XWmgPUepQ7LuvudDLTmojA5KeJrS7gJ7Ef8uSH26i1ifwCkhUI03Rb34Wdn8BVP6U2oQdvr9nP+D4d6JkU53Qy09okD0eG3c1tno+oO7bdliYNUFYgTNMUH4KPH4VuGXDZt1m4/TiHiyu587IeTiczrdXEJ5DIeJ5r8ya/+2wHh4sqnE5kGrACYc6trhbe/5b389deQl1uXlyWyyXto5nU3xb6MxcoNhGZ+DjpVZuYzBf8fG6W04lMA1YgzLkt/DnsXQ7TfguJvVi37wQb9xfxzXE9bXDaXJzh90GXIfwi8k3Wb9vFp1ttttdA4tefbhGZLCLZIpIjIo81sj9SRGb59q8WkRTf9kQR+VxESkXkeX9mNOew+R1Y9TyMfAiG3AHAn5fk0jYmnFsykh0OZ1o9lxtu+CPRnlL+EP8qT3ywlZOVNU6nMj5+KxAi4gZeAKYA6cDtIpLeoNkDwAlV7Q08Czzt214J/Az4ob/ymSY4sgnmPgI9xsK1vwJg04EiFu04zjfGphIT4dclzU2o6DQAufKnjKtZxejyRfzPx3ZvRKDw5xnESCBHVXerajUwE5jRoM0M4FXf4znARBERVS1T1RV4C4VxQtF+eOs2iEmEW14Ft3cajd8t2Em7mHC+MS7V4YAmqIz5Llwyil9HvcaStZksyT7udCKDfwtEN+BAvecHfdsabaOqtUAxkNjUNxCRh0RknYisy8vLu8i45iulx+G1G6CmDO6YDXFJAKzMzWfZzjy+fUUv4iLt7ME0I5cbbvwTkS7lpdg/89M5GymusK4mp7XqEUZVfUlVM1Q1Iykpyek4waGiCN74Gpw8Ane8A50HAlBT5+G/524juV0094xOcTSiCVLteyLXP8eldVk8XPkXnvxwm9OJQp4/C8Qh4JJ6z5N92xptIyJhQAJQ4MdM5mwqiuDNm+H4Drj1deh+2Ve7Xl+1j+xjJ/nZdelEhbudy2iC26W3wNjvc7d7ARGbXmXeliNOJwpp/iwQa4E+IpIqIhHAbcDcBm3mAvf6Ht8MLFZVW7DWCWX58Op1cDgTbvk79J701a69+WX8Zn42l/dN4pr0Ts5lNKFh4s/x9L6aX4S/wjvvzrQZXx3ktwLhG1N4BJgPbAdmq2qWiDwpItN9zV4GEkUkB3gU+OpSWBHZCzwD3CciBxu5Aso0l5LD8PepkL8Lbp/pnUzNp7bOw3+8s4lwt/DUTYMQEQeDmpDgcuO6+WW0XSp/0Kf5w+vv2FxNDpFg+YM9IyND161b53SM1ufEPnhtuvcM4o5ZkDLuX3b/4qNtvLxiD8/dNoQZQ2zNB9OCig9S/uerqSovYd6lz3PnTTc6nSgoich6Vc1obF+rHqQ2F+lYFvxtsnfs4Z65pxWH2WsP8PKKPdw3JsWKg2l5CcnEPDgPjYhjxuaHyVzyntOJQo4ViFCV+7m3OKBw38eQPPxfdn+46TCPvbeZ8X068NNp/Z3JaEz7VKIfXkieuxMDlzxA/sLfe6edNy3CCkQo2vC692qltt3hm4u+upT1lNdX7eUHszLJSGnPS3dnEG7zLRkHRSdeQvhDC1nBMDqs+Dm1r33NO7uw8Tv7yQ8lqrD4l97pM1InwP2fQMI/u45OVtbwo3c28bMPsrgyLYm/3zeC6Ai7pNU4L7lzRyLvmsnPa++nbu9K9I+jYMNr4KlzOlpQswIRKmqr4L0HYdlvYNg93juko9oA4PEoH2QeYvLvl/PuhoM8cmVvXrw7g1i7W9oEkNG9k+g17QdcXflr9rhTYe534c/jYMfH1u3kJ/YbIBSU5cPse2DfFzDxcRj3KIhQXF7Dh5sP88rKveQcLyW9Sxv+cPsQhvdo73RiYxp1z+gUjpeMZeLnSTw7YA83nHgFZt4BXYfCqO9A+g0QFuF0zKBhBSLYHdkMM++E0mNU3/AX1sVfxeqFu1i9p4AN+4qorvOQ3qUNz902hOsv7YrLZfc5mMD2H9f0paiimh986eLINW/x7YTVsPIP3jPkz/4LMh6AjPshzhazulh2H0QQq940B/fcRyh3x/M/8f/Fe0c7Ul3nwSUwoGsCo3slcv2lXRnYrY3dAGdaFY9H+cGsTOZuOswjV/bmP67ujeR+Dqv/DDkLwB0BA2/yrmPSbZjTcQPa2e6DsDOIIHOoqIL5Ww7TYe1vmF7yNus8fflO+Q/o0qYH941NZFTP9mSktKdNVLjTUY25YC6X8MzXBxMT4eb5z3MoKKvilzdMxN1nEuTnwJoXIfMt2PQ2JI+Ey74F6TO+mrbeNI2dQQSB6loPn207yqy1B9iek8szYX9kgnsLGzpcT8mVv2Z4r87EW0EwQUhV+e1n2bzweS6T+nfimVsH//OPn8oSb5FY8yIU7oa4zjDiARjxTYixcbZTznYGYQWiFauu9TBn/UFe+DyHQ0UVTIvP4Wn+QIynFNeUp7zr/VrXkQkBr67cyy8+2ka3dtG8cMcwBnZL+OdOjwdyF/m6nxZCRDyMfBBG/xvEdnAudICwAhFkVJW5mw7zv59mc6iogmHJ8fy28wJSs55H2veCW1457eY3Y4Ldur2FPPLWRgrLq/mvaf2567Iep190cSzLe6l31j8gPBoyvgFjvgfxoTtLsRWIIJJz/CSPf5DFytwCBnZrw0/HxDBqy+PI3hVw6a0w7RmIjHM6pjGOKCit4tHZm1i6M4/hPdrxqxsHkdY5/vSGedmw/Hew5R1wR3q7nsZ+PySvfLICEQTKq2v5w6IcXl6xm5iIMH50TR/ucC3AtfAJEBdMeQqG3GldSibkqSrvbTjELz/exsnKWu4fm8K3r+hN+9hG7o8oyPWeUWyeBWFR3vGJsd8Pqa4nKxCtmKoyP+soT364jcPFldwyPJmfDq2k7dKfwYHV0GsiXP8ctL3k3F/MmBBSWFbNU59s5531B4mNCOOBcak8MD618Sv48nNg2f96zyjCor1jFGO+B7GJLR+8hVmBaKX25JfxxNwslu7Mo1/neJ6+JonBO5+HzDe9f+FMesLOGow5h13HTvLMgp18svUoMRFubh6ezL1jUuiV1EhXbN5OX6GYAxGx3vsoxnw3qK96sgLRypRW1fLS0lz+vHQ3EWEunhgTwdcq3sW1ZZZ3zplR34YJP/pqLiVjzLltPVTM377Yw0ebjlBd52FkanumD+7K1EFdTu9+ysuGpU/D1vcgIs47f9nIB6F9qjPh/cgKRCtRVlXLa6v28dKyXGrKi3ksNZdbIlYRuW8phEV6zxbGfDco/5Ma01LyTlYxa+1+3t94iNy8MsJcwrDu7RjXpwPj+nRgYNcEIsJ885ge3+4dzM563ztzbNpUGHa3d832ILnpzgpEgMs5fpI5q3PJ2riS9KrNTI3PYVDNZlx1Vd41GwbfDiMehLgkp6MaEzRUlW1HSvh48xGW7cpj66ESAMLdQlrneAZ0SaBHhxiS28WQEl5Mr30zidnyBlKeDzEdYNDN0G8adB/dqouFYwVCRCYDzwFu4K+q+lSD/ZHAa8BwoAC4VVX3+vb9GHgAqAO+p6rzz/ZeAV8gVKG8AEoOUXviAIf253Ls4G6q8nbTqSKXVDlKuPjmtu+QBj2v8P4HTB5hYwzGtICC0iq+3F3I5kNFZB0qYfuREgrKqv+lTbjUMiUyi6+5lzO2bg3h1FLhimV3mxHktR1KecehaOdLadcmnnaxESTGRtAuNiKgF91ypECIiBvYCVwNHATWArer6rZ6bb4DXKqqD4vIbcCNqnqriKQDbwMjga7AQqCvqp5xdZBmKxCqoB7v6aTWNfjsgZoKqCmH6lKoLkery6irLKWuqgxPRTGe8gIoL0TLC/GUFSAVhbgrTxBRXUSY/ut/thp1U+DuQFX7NDr0GkZs9yHQY2xIXottTCAqq6rlUFEFh05UcPBEOXml1RSXV1NUUUN5aQmpxWu4tGI1Q2sz6UYeALXq4oAmsUe7sE87kadtKQ1vR01ke+piOhAV04bI6BiiomOJiYklNjaWuNg4EmIiSYgOJyrcTWSYi4gwF5FhbiJ8jyPcLsLd0uwTazo1Wd9IIEdVd/tCzARmANvqtZkBPOF7PAd4Xrzf/QxgpqpWAXtEJMf39VY1e8rDG+FvU/61GJwHwXsQTx3IOhWKiKNI4ygkniKN54R2ppB4yiOTcLe9hNikHqSk9mH4gDQ6x0U193dkjGkmsZFh9O0UT99OjdxsB8DlXz2qLTpM+Z7V1BzcSHz+LjKKchlfupzwunJvgyrfx4nGv5JHBQ/ej9uqf8YG7XvGXC4BlwguEURg2qAuPHPrkAv5Fs/KnwWiG3Cg3vODwGVnaqOqtSJSDCT6tn/Z4LXdGrwWEXkIeMj3tFREsi8gZwcg/wJedxbFzfFF/JCrWViupgvETGC5zodDmR49V4N/ybUTePa2C36zHmfa0aqn+1bVl4CXLuZriMi6M51eOclynZ9AzBWImcBynY9AzAQtl8ufIyeHgPq39yb7tjXaRkTCgAS8g9VNea0xxhg/8meBWAv0EZFUEYkAbgPmNmgzF7jX9/hmYLF6R83nAreJSKSIpAJ9gDV+zGqMMaYBv3Ux+cYUHgHm473M9W+qmiUiTwLrVHUu8DLwum8QuhBvEcHXbjbeAe1a4N/OdgXTRbqoLio/slznJxBzBWImsFznIxAzQQvlCpob5YwxxjSvwL17wxhjjKOsQBhjjGlUSBUIEYkSkTUisklEskTkv33bU0VktYjkiMgs36B6IOR6RUT2iEim72NIS+byZXCLyEYR+cj33NFjdZZcgXCs9orIFt/7r/Ntay8iC0Rkl+9zuwDI9ISIHKp3rKa2ZCZfhrYiMkdEdojIdhEZ7fSxOksuR4+XiKTVe+9MESkRkR+0xPEKqQKB9z7Gq1R1MDAEmCwio4CngWdVtTfe+xwfCJBcAD9S1SG+j8wWzgXwfWB7vedOH6tTGuYC548VwJW+9z91jfpjwCJV7QMs8j13OhN4/w1PHat5DmR6DvhUVfsBg/H+WwbCsWosFzh4vFQ1+9R74523rhx4nxY4XiFVINSr1Pc03PehwFV4p/oAeBW4IUByOUpEkoFpwF99zwWHj1VjuQLcDLzHCRw6XoFGRBKACXivYkRVq1W1CIeP1VlyBZKJQK6q7qMFjldIFQj4qmsiEzgOLABygSJVrfU1aXRaj5bOpaqrfbv+R0Q2i8iz4p39tiX9Hvh/gMf3PJEAOFaN5DrFyWMF3qL+mYisF+80MACdVPWI7/FRoFMAZAJ4xHes/uZAV04qkAf83ddN+FcRicX5Y3WmXODs8arvNrwTmUILHK+QKxCqWuc7VUvGOwFgP2cTeTXMJSIDgR/jzTcCaA/8Z0vlEZHrgOOqur6l3rMpzpLLsWNVzzhVHQZMAf5NRCbU3+m7CbSlzwwby/QnoBfe7swjwO9aOFMYMAz4k6oOBcpo0D3i0LE6Uy6njxcAvvG+6cA7Dff563iFXIE4xXfq+DkwGmgr3qk+wOFpPerlmqyqR3zdT1XA3/EWtJYyFpguInuBmXi7lp7D+WN1Wi4RecPhYwWAqh7yfT6Ot494JHBMRLoA+D4fdzqTqh7z/UHiAf5Cyx+rg8DBemfJc/D+Ynb0WJ0pVwAcr1OmABtU9Zjvud+PV0gVCBFJEpG2vsfReNeq2I73F/LNvmb3Ah8EQK4d9f7xBW//4taWyqSqP1bVZFVNwXtau1hV78ThY3WGXHc5eax87xsrIvGnHgPX+DLUn06mRY/XmTKdOlY+N9LCx0pVjwIHRCTNt2ki3lkTHDtWZ8vl9PGq53b+2b0ELXC8WvVsrhegC/CqeBczcgGzVfUjEdkGzBSRXwIb8Q1SBUCuxSKShHfZiUzg4RbO1Zj/xNljdSZvOnysOgHve+sTYcBbqvqpiKwFZovIA8A+4OsBkOl18V4GrMBe4FstmOmU7+L9N4sAdgP34/u/79CxOluuPzh9vHwF/uoG7/0Ufj5eNtWGMcaYRoVUF5MxxpimswJhjDGmUVYgjDHGNMoKhDHGmEZZgTDGGNMoKxDGGGMaZQXCGGNMo/4/1Y0loZaSK7gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.normal(loc=50, scale=5, size=1000), hist=False, label='normal')\n",
    "sns.distplot(random.binomial(n=100, p=0.5, size=1000), hist=False, label='binomial')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585f09f4",
   "metadata": {},
   "source": [
    "# 泊松分布\n",
    "泊松分布是一种离散分布。\n",
    "\n",
    "例如，如果某人一天吃两次，他吃三次的概率是多少？\n",
    "\n",
    "它有两个参数。\n",
    "\n",
    "lam - 速率或已知的发生次数，例如上面的问题是2。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d80f0b88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 0 3 2 5 3 3 1 3 2]\n"
     ]
    }
   ],
   "source": [
    "x = random.poisson(lam=2, size=10)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dee64d8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.poisson(lam=2, size=1000), kde=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29289761",
   "metadata": {},
   "source": [
    "正态分布和泊松分布的区别\n",
    "\n",
    "正态分布是连续的，而泊松分布是离散的。\n",
    "\n",
    "但我们可以看到，与二项分布相似，对于一个足够大的泊松分布，它将变得与正态分布相似，具有一定的标准差和平均数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b072ed3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.normal(loc=50, scale=7, size=1000), hist=False, label='normal')\n",
    "sns.distplot(random.poisson(lam=50, size=1000), hist=False, label='poisson')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497d963b",
   "metadata": {},
   "source": [
    "泊松分布和二项分布的区别\n",
    "\n",
    "二项分布的区别非常微妙，它是指二项分布是针对离散试验的，而泊松分布是针对连续试验的。\n",
    "\n",
    "但对于非常大的n和接近零的p来说，二项分布与泊松分布几乎相同，如n*p几乎等于lam。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a793f43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.binomial(n=1000, p=0.01, size=1000), hist=False, label='binomial')\n",
    "sns.distplot(random.poisson(lam=10, size=1000), hist=False, label='poisson')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd409c2c",
   "metadata": {},
   "source": [
    "# 均匀分布\n",
    "用于描述每个事件发生的机会均等的概率。\n",
    "\n",
    "例如，随机数的产生。\n",
    "\n",
    "它有三个参数。\n",
    "\n",
    "a - 下限 - 默认为0 .0。\n",
    "\n",
    "b - 上限 - 默认为1.0。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "72f8303d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.1304756  0.78673227 0.58180018]\n",
      " [0.11130861 0.8883046  0.31735287]]\n"
     ]
    }
   ],
   "source": [
    "x = random.uniform(size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "856d1016",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.uniform(size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d107104",
   "metadata": {},
   "source": [
    "# Logistic分布\n",
    "Logistic分布是用来描述增长的。\n",
    "\n",
    "在机器学习中广泛用于逻辑回归、神经网络等。\n",
    "\n",
    "它有三个参数。\n",
    "\n",
    "loc - 平均值，峰值在哪里。默认为0。\n",
    "\n",
    "scale - 标准差，分布的平坦程度。默认为1。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fbc4feb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.48817887  7.81663577  2.24676853]\n",
      " [ 0.76414206 -2.72540463  1.72524622]]\n"
     ]
    }
   ],
   "source": [
    "x = random.logistic(loc=1, scale=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "680bb6b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.logistic(size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655000a3",
   "metadata": {},
   "source": [
    "Logistic分布和正态分布的区别\n",
    "\n",
    "两种分布几乎相同，但Logistic分布的尾部面积更大，也就是说，它表示离平均值更远的事件发生的可能性更大。\n",
    "\n",
    "对于较高的尺度值（标准差），除了峰值之外，正态分布和Logistic分布几乎是相同的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "66f53ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.normal(scale=2, size=1000), hist=False, label='normal')\n",
    "sns.distplot(random.logistic(size=1000), hist=False, label='logistic')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "029f0536",
   "metadata": {},
   "source": [
    "# 多项式分布\n",
    "多项分布是二项分布的一个概括。\n",
    "\n",
    "它描述的是多种情况下的结果，与二项分布不同，二项分布的情况必须是两种中的一种。\n",
    "\n",
    "它有三个参数。\n",
    "\n",
    "n--可能的结果数（例如，掷骰子的结果为6）。\n",
    "\n",
    "pvals - 结果的概率列表（例如：掷骰子的结果为[1/6, 1/6, 1/6, 1/6, 1/6]）。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "32fc9d54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "x = random.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6])\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48611b5",
   "metadata": {},
   "source": [
    "# 指数分布\n",
    "指数分布用于描述到下一个事件的时间，如失败/成功等。\n",
    "\n",
    "它有两个参数。\n",
    "\n",
    "scale - 速率的倒数（见poisson分布中的lam），默认为1.0。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b5909a1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.55414446 0.16565391 0.11404409]\n",
      " [1.27323337 0.69660822 1.14820704]]\n"
     ]
    }
   ],
   "source": [
    "x = random.exponential(scale=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e47f22f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.exponential(size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a0e61b0",
   "metadata": {},
   "source": [
    "泊松分布和指数分布之间的关系\n",
    "\n",
    "泊松分布处理的是一个时间段内发生的事件的数量，而指数分布处理的是这些事件之间的时间。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7657c696",
   "metadata": {},
   "source": [
    "# 智方分布\n",
    "智方分布被用来作为验证假设的基础。\n",
    "\n",
    "它有两个参数。\n",
    "\n",
    "df - （自由度）。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "02617964",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.36598405 0.6437793  0.41594131]\n",
      " [8.60570647 0.65662278 0.5759674 ]]\n"
     ]
    }
   ],
   "source": [
    "x = random.chisquare(df=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "da513318",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.chisquare(df=1, size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501ab69c",
   "metadata": {},
   "source": [
    "# 雷利分布\n",
    "雷利分布被用于信号处理中。\n",
    "\n",
    "它有两个参数。\n",
    "\n",
    "scale - （标准偏差）决定分布的平坦程度，默认为1.0）。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "74488512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.52095435 1.35780099 2.00132874]\n",
      " [2.16729614 2.32890294 1.76518953]]\n"
     ]
    }
   ],
   "source": [
    "x = random.rayleigh(scale=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8b690d0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.rayleigh(size=1000), hist=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b003a0f",
   "metadata": {},
   "source": [
    "雷利分布和智方分布的相似性\n",
    "\n",
    "在单位stddev和2个自由度的情况下，雷利分布和智平方分布代表相同的分布。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138b0477",
   "metadata": {},
   "source": [
    "# 帕累托分布\n",
    "遵循帕累托法则的分布，即80-20分布（20%的因素导致80%的结果）。\n",
    "\n",
    "它有两个参数。\n",
    "\n",
    "a - 形状参数。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d84c481b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.90662803e-01 8.37361744e-01 4.27001610e-01]\n",
      " [1.12293033e-03 1.34791944e+00 2.61951282e-01]]\n"
     ]
    }
   ],
   "source": [
    "x = random.pareto(a=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3e69abcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\pandascourse\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(random.pareto(a=2, size=1000), kde=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22e0de56",
   "metadata": {},
   "source": [
    "# 拉兹夫分布\n",
    "拉兹夫分布是根据Zipf定律对数据进行抽样。\n",
    "\n",
    "拉兹夫定律：在一个集合中，第n个常用词是最常用词的1/n倍。例如，英语中第5个常用词的出现率几乎是最常用词的1/5。\n",
    "\n",
    "它有两个参数。\n",
    "\n",
    "a - 分布参数。\n",
    "\n",
    "size - 返回数组的形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b2ab4628",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 2]\n",
      " [2 1 1]]\n"
     ]
    }
   ],
   "source": [
    "x = random.zipf(a=2, size=(2, 3))\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f74b8e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = random.zipf(a=2, size=1000)\n",
    "sns.distplot(x[x<10], kde=False)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
